The Upper Ganga River basin is socioeconomically the most important river basin in India and is highly stressed in terms of water resources due to uncontrolled land use and land cover (LULC) activities. This study presents a comprehensive set of analyses to evaluate the population growth, LULC transformations, and water quality nexus for sustainable development in this river basin. The study was conducted at two spatial scales: basin scale and district scale. First, population data were analyzed statistically to study demographic changes, followed by LULC change detection over the period of February–March 2001 to 2012 (Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data) using remote sensing and geographical information system (GIS) techniques. Trends and spatiotemporal variations in monthly water quality parameters, viz. biological oxygen demand (BOD), dissolved oxygen (DO, measured in percentage), fluoride (F), hardness (CaCO₃), pH, total coliform bacteria and turbidity, were studied using the Mann–Kendall rank test and an overall index of pollution (OIP) developed specifically for this region, respectively. A relationship was deciphered between LULC classes and OIP using multivariate techniques, viz. Pearson's correlation and multiple linear regression. From the results, it was observed that population has increased in the river basin. Therefore, significant and characteristic LULC changes were observed. The river became polluted in both rural and urban areas. In rural areas, pollution is due to agricultural practices, mainly fertilizers, whereas in urban areas it is mainly contributed from domestic and industrial wastes. Water quality degradation has occurred in the river basin, and consequently the health status of the river has also changed from acceptable to slightly polluted in urban areas. Multiple linear regression models developed for the Upper Ganga River basin could successfully predict status of the water quality, i.e., OIP, using LULC classes.

The upper Ganga Basin in Uttarakhand, India, has high hydropower potential and plays an important role in the development of the state economy. Thus, an accurate knowledge of annual water yield is of paramount importance to this region. This paper deals with use of contemporary water yield estimation models such as the distributed Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the Lumped Zhang model and their validation to identify the most suited one for water yield estimation in the upper Ganga Basin. In previous studies utilizing these models, water yield was estimated by considering a single value of some important model parameters for the entire basin, which in fact show distributed variation at a finer (pixel) scale. Therefore, in this study, pixel-level computations are performed to assess and ascertain the need for incorporating the spatial variation of such parameters in model applications. To validate the findings, the observed sub-basin discharge data are analyzed with the computed water yield for 4 decades, i.e., 1980, 1990, 2001 and 2015. The results obtained are in good agreement with the water yield obtained at the pixel scale.

Hydrological responses of a watershed are often cumbersome to identify because they exhibit spatiotemporal variation. Rather, it is interesting to observe the hydrologic responses of a basin at a microlevel scale. Thus, a study to incorporate spatial variability through the use of remote sensing techniques in water resources management would be interesting. This study used various watersheds in the state of Uttarakhand, India to compute water yield using simplified models, such as the lumped Zhang model, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and the more comprehensive and parameter-intensive Soil and Water Assessment Tool (SWAT) model. The focus of the study was to gain knowledge about the methodology to adopt for computing water yield if it has to be implemented on diverse watersheds. Nine diverse watersheds were considered, varying in precipitation pattern, temperature, area, topography, discharge, and climatic conditions. The study clearly indicates whether to choose the lumped Zhang model, which provides the average values for the entire basin, a pixel-level assessment model, or a more comprehensive SWAT model. The methods were assessed for their applicability in pre– and post–climate change scenarios.